US
deaths <- read.csv.zoo("us/us-deaths.csv", FUN=as.yearmon)
deaths <- window(deaths,
start=as.yearmon("Jan 2010"),
end=as.yearmon("Oct 2020"))
go(deaths)
Alabama
During the post-intervention period, the response variable had an average value of approx. 5.10K. By contrast, in the absence of an intervention, we would have expected an average response of 4.46K. The 95% interval of this counterfactual prediction is [4.35K, 4.57K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.64K with a 95% interval of [0.52K, 0.75K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 50.95K. By contrast, had the intervention not taken place, we would have expected a sum of 44.58K. The 95% interval of this prediction is [43.48K, 45.72K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+12%, +17%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.64K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Alaska
During the post-intervention period, the response variable had an average value of approx. 398.60. By contrast, in the absence of an intervention, we would have expected an average response of 361.66. The 95% interval of this counterfactual prediction is [339.15, 381.88]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 36.94 with a 95% interval of [16.72, 59.45]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 3.99K. By contrast, had the intervention not taken place, we would have expected a sum of 3.62K. The 95% interval of this prediction is [3.39K, 3.82K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +10%. The 95% interval of this percentage is [+5%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (36.94) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Arizona
During the post-intervention period, the response variable had an average value of approx. 6.22K. By contrast, in the absence of an intervention, we would have expected an average response of 5.00K. The 95% interval of this counterfactual prediction is [4.83K, 5.17K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.21K with a 95% interval of [1.04K, 1.38K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 62.19K. By contrast, had the intervention not taken place, we would have expected a sum of 50.05K. The 95% interval of this prediction is [48.35K, 51.74K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +24%. The 95% interval of this percentage is [+21%, +28%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.21K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Arkansas
During the post-intervention period, the response variable had an average value of approx. 3.02K. By contrast, in the absence of an intervention, we would have expected an average response of 2.68K. The 95% interval of this counterfactual prediction is [2.59K, 2.76K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.34K with a 95% interval of [0.26K, 0.43K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 30.21K. By contrast, had the intervention not taken place, we would have expected a sum of 26.80K. The 95% interval of this prediction is [25.93K, 27.58K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +13%. The 95% interval of this percentage is [+10%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.34K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
California
During the post-intervention period, the response variable had an average value of approx. 25.53K. By contrast, in the absence of an intervention, we would have expected an average response of 22.31K. The 95% interval of this counterfactual prediction is [21.78K, 22.85K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 3.21K with a 95% interval of [2.68K, 3.74K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 255.26K. By contrast, had the intervention not taken place, we would have expected a sum of 223.13K. The 95% interval of this prediction is [217.83K, 228.51K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+12%, +17%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (3.21K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Colorado
During the post-intervention period, the response variable had an average value of approx. 3.79K. By contrast, in the absence of an intervention, we would have expected an average response of 3.26K. The 95% interval of this counterfactual prediction is [3.17K, 3.35K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.53K with a 95% interval of [0.44K, 0.62K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 37.90K. By contrast, had the intervention not taken place, we would have expected a sum of 32.57K. The 95% interval of this prediction is [31.70K, 33.55K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +16%. The 95% interval of this percentage is [+13%, +19%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.53K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Connecticut
During the post-intervention period, the response variable had an average value of approx. 3.08K. By contrast, in the absence of an intervention, we would have expected an average response of 2.58K. The 95% interval of this counterfactual prediction is [2.52K, 2.65K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.50K with a 95% interval of [0.44K, 0.57K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 30.84K. By contrast, had the intervention not taken place, we would have expected a sum of 25.84K. The 95% interval of this prediction is [25.19K, 26.48K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+17%, +22%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.50K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Delaware
During the post-intervention period, the response variable had an average value of approx. 889.30. By contrast, in the absence of an intervention, we would have expected an average response of 770.44. The 95% interval of this counterfactual prediction is [742.88, 799.35]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 118.86 with a 95% interval of [89.95, 146.42]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 8.89K. By contrast, had the intervention not taken place, we would have expected a sum of 7.70K. The 95% interval of this prediction is [7.43K, 7.99K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+12%, +19%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (118.86) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
District.of.Columbia
During the post-intervention period, the response variable had an average value of approx. 618.70. By contrast, in the absence of an intervention, we would have expected an average response of 403.49. The 95% interval of this counterfactual prediction is [386.62, 419.73]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 215.21 with a 95% interval of [198.97, 232.08]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 6.19K. By contrast, had the intervention not taken place, we would have expected a sum of 4.03K. The 95% interval of this prediction is [3.87K, 4.20K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +53%. The 95% interval of this percentage is [+49%, +58%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (215.21) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Florida
During the post-intervention period, the response variable had an average value of approx. 20.27K. By contrast, in the absence of an intervention, we would have expected an average response of 17.28K. The 95% interval of this counterfactual prediction is [16.88K, 17.67K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 2.99K with a 95% interval of [2.59K, 3.38K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 202.66K. By contrast, had the intervention not taken place, we would have expected a sum of 172.81K. The 95% interval of this prediction is [168.82K, 176.71K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +17%. The 95% interval of this percentage is [+15%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (2.99K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Georgia
During the post-intervention period, the response variable had an average value of approx. 8.42K. By contrast, in the absence of an intervention, we would have expected an average response of 7.10K. The 95% interval of this counterfactual prediction is [6.92K, 7.29K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.32K with a 95% interval of [1.13K, 1.50K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 84.19K. By contrast, had the intervention not taken place, we would have expected a sum of 71.01K. The 95% interval of this prediction is [69.16K, 72.92K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+16%, +21%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.32K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Hawaii
During the post-intervention period, the response variable had an average value of approx. 1.01K. By contrast, in the absence of an intervention, we would have expected an average response of 0.96K. The 95% interval of this counterfactual prediction is [0.93K, 0.99K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.06K with a 95% interval of [0.03K, 0.09K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 10.15K. By contrast, had the intervention not taken place, we would have expected a sum of 9.56K. The 95% interval of this prediction is [9.27K, 9.86K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +6%. The 95% interval of this percentage is [+3%, +9%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.06K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Idaho
During the post-intervention period, the response variable had an average value of approx. 1.32K. By contrast, in the absence of an intervention, we would have expected an average response of 1.19K. The 95% interval of this counterfactual prediction is [1.15K, 1.23K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.13K with a 95% interval of [0.09K, 0.17K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 13.19K. By contrast, had the intervention not taken place, we would have expected a sum of 11.89K. The 95% interval of this prediction is [11.50K, 12.30K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +11%. The 95% interval of this percentage is [+7%, +14%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.13K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Illinois
During the post-intervention period, the response variable had an average value of approx. 10.26K. By contrast, in the absence of an intervention, we would have expected an average response of 8.99K. The 95% interval of this counterfactual prediction is [8.77K, 9.22K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.27K with a 95% interval of [1.04K, 1.49K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 102.64K. By contrast, had the intervention not taken place, we would have expected a sum of 89.94K. The 95% interval of this prediction is [87.73K, 92.20K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+12%, +17%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.27K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Indiana
During the post-intervention period, the response variable had an average value of approx. 6.26K. By contrast, in the absence of an intervention, we would have expected an average response of 5.45K. The 95% interval of this counterfactual prediction is [5.30K, 5.61K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.81K with a 95% interval of [0.65K, 0.95K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 62.55K. By contrast, had the intervention not taken place, we would have expected a sum of 54.50K. The 95% interval of this prediction is [53.01K, 56.10K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+12%, +18%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.81K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Iowa
During the post-intervention period, the response variable had an average value of approx. 2.79K. By contrast, in the absence of an intervention, we would have expected an average response of 2.50K. The 95% interval of this counterfactual prediction is [2.43K, 2.57K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.29K with a 95% interval of [0.22K, 0.36K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 27.93K. By contrast, had the intervention not taken place, we would have expected a sum of 25.01K. The 95% interval of this prediction is [24.30K, 25.73K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +12%. The 95% interval of this percentage is [+9%, +15%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.29K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Kansas
During the post-intervention period, the response variable had an average value of approx. 2.42K. By contrast, in the absence of an intervention, we would have expected an average response of 2.26K. The 95% interval of this counterfactual prediction is [2.20K, 2.33K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.16K with a 95% interval of [0.09K, 0.22K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 24.19K. By contrast, had the intervention not taken place, we would have expected a sum of 22.64K. The 95% interval of this prediction is [22.03K, 23.31K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +7%. The 95% interval of this percentage is [+4%, +10%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.16K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Kentucky
During the post-intervention period, the response variable had an average value of approx. 4.42K. By contrast, in the absence of an intervention, we would have expected an average response of 4.05K. The 95% interval of this counterfactual prediction is [3.95K, 4.16K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.37K with a 95% interval of [0.26K, 0.47K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 44.20K. By contrast, had the intervention not taken place, we would have expected a sum of 40.52K. The 95% interval of this prediction is [39.47K, 41.55K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +9%. The 95% interval of this percentage is [+7%, +12%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.37K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Louisiana
During the post-intervention period, the response variable had an average value of approx. 4.68K. By contrast, in the absence of an intervention, we would have expected an average response of 3.77K. The 95% interval of this counterfactual prediction is [3.65K, 3.88K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.91K with a 95% interval of [0.79K, 1.03K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 46.77K. By contrast, had the intervention not taken place, we would have expected a sum of 37.71K. The 95% interval of this prediction is [36.46K, 38.85K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +24%. The 95% interval of this percentage is [+21%, +27%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.91K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Maine
During the post-intervention period, the response variable had an average value of approx. 1.28K. By contrast, in the absence of an intervention, we would have expected an average response of 1.23K. The 95% interval of this counterfactual prediction is [1.18K, 1.27K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.06K with a 95% interval of [0.02K, 0.10K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 12.82K. By contrast, had the intervention not taken place, we would have expected a sum of 12.25K. The 95% interval of this prediction is [11.79K, 12.67K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +5%. The 95% interval of this percentage is [+1%, +8%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.06K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.005). This means the causal effect can be considered statistically significant.
Maryland
During the post-intervention period, the response variable had an average value of approx. 4.90K. By contrast, in the absence of an intervention, we would have expected an average response of 4.20K. The 95% interval of this counterfactual prediction is [4.09K, 4.31K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.70K with a 95% interval of [0.59K, 0.81K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 48.99K. By contrast, had the intervention not taken place, we would have expected a sum of 42.03K. The 95% interval of this prediction is [40.90K, 43.12K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +17%. The 95% interval of this percentage is [+14%, +19%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.70K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Massachusetts
During the post-intervention period, the response variable had an average value of approx. 5.77K. By contrast, in the absence of an intervention, we would have expected an average response of 4.85K. The 95% interval of this counterfactual prediction is [4.71K, 4.98K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.92K with a 95% interval of [0.79K, 1.06K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 57.74K. By contrast, had the intervention not taken place, we would have expected a sum of 48.53K. The 95% interval of this prediction is [47.11K, 49.83K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+16%, +22%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.92K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Michigan
During the post-intervention period, the response variable had an average value of approx. 9.31K. By contrast, in the absence of an intervention, we would have expected an average response of 8.18K. The 95% interval of this counterfactual prediction is [7.97K, 8.38K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.13K with a 95% interval of [0.93K, 1.34K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 93.11K. By contrast, had the intervention not taken place, we would have expected a sum of 81.82K. The 95% interval of this prediction is [79.70K, 83.83K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+11%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.13K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Minnesota
During the post-intervention period, the response variable had an average value of approx. 4.14K. By contrast, in the absence of an intervention, we would have expected an average response of 3.74K. The 95% interval of this counterfactual prediction is [3.64K, 3.84K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.40K with a 95% interval of [0.30K, 0.50K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 41.35K. By contrast, had the intervention not taken place, we would have expected a sum of 37.38K. The 95% interval of this prediction is [36.37K, 38.36K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +11%. The 95% interval of this percentage is [+8%, +13%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.40K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Mississippi
During the post-intervention period, the response variable had an average value of approx. 3.18K. By contrast, in the absence of an intervention, we would have expected an average response of 2.69K. The 95% interval of this counterfactual prediction is [2.61K, 2.76K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.49K with a 95% interval of [0.42K, 0.57K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 31.80K. By contrast, had the intervention not taken place, we would have expected a sum of 26.90K. The 95% interval of this prediction is [26.14K, 27.62K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +18%. The 95% interval of this percentage is [+16%, +21%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.49K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Missouri
During the post-intervention period, the response variable had an average value of approx. 6.00K. By contrast, in the absence of an intervention, we would have expected an average response of 5.15K. The 95% interval of this counterfactual prediction is [5.02K, 5.29K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.84K with a 95% interval of [0.71K, 0.98K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 59.96K. By contrast, had the intervention not taken place, we would have expected a sum of 51.54K. The 95% interval of this prediction is [50.18K, 52.88K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +16%. The 95% interval of this percentage is [+14%, +19%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.84K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Montana
During the post-intervention period, the response variable had an average value of approx. 947.60. By contrast, in the absence of an intervention, we would have expected an average response of 837.25. The 95% interval of this counterfactual prediction is [806.68, 869.01]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 110.35 with a 95% interval of [78.59, 140.92]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 9.48K. By contrast, had the intervention not taken place, we would have expected a sum of 8.37K. The 95% interval of this prediction is [8.07K, 8.69K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +13%. The 95% interval of this percentage is [+9%, +17%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (110.35) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Nebraska
During the post-intervention period, the response variable had an average value of approx. 1.54K. By contrast, in the absence of an intervention, we would have expected an average response of 1.38K. The 95% interval of this counterfactual prediction is [1.33K, 1.42K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.17K with a 95% interval of [0.12K, 0.21K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 15.43K. By contrast, had the intervention not taken place, we would have expected a sum of 13.76K. The 95% interval of this prediction is [13.29K, 14.20K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +12%. The 95% interval of this percentage is [+9%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.17K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Nevada
During the post-intervention period, the response variable had an average value of approx. 2.50K. By contrast, in the absence of an intervention, we would have expected an average response of 2.11K. The 95% interval of this counterfactual prediction is [2.04K, 2.18K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.38K with a 95% interval of [0.31K, 0.46K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 24.97K. By contrast, had the intervention not taken place, we would have expected a sum of 21.13K. The 95% interval of this prediction is [20.41K, 21.84K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +18%. The 95% interval of this percentage is [+15%, +22%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.38K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
New.Hampshire
During the post-intervention period, the response variable had an average value of approx. 1.11K. By contrast, in the absence of an intervention, we would have expected an average response of 1.06K. The 95% interval of this counterfactual prediction is [1.02K, 1.10K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.05K with a 95% interval of [0.01K, 0.09K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 11.06K. By contrast, had the intervention not taken place, we would have expected a sum of 10.58K. The 95% interval of this prediction is [10.16K, 10.98K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +5%. The 95% interval of this percentage is [+1%, +9%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.05K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.008). This means the causal effect can be considered statistically significant.
New.Jersey
During the post-intervention period, the response variable had an average value of approx. 7.99K. By contrast, in the absence of an intervention, we would have expected an average response of 6.16K. The 95% interval of this counterfactual prediction is [6.00K, 6.30K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.83K with a 95% interval of [1.68K, 1.99K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 79.85K. By contrast, had the intervention not taken place, we would have expected a sum of 61.59K. The 95% interval of this prediction is [59.96K, 63.02K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +30%. The 95% interval of this percentage is [+27%, +32%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.83K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
New.Mexico
During the post-intervention period, the response variable had an average value of approx. 1.75K. By contrast, in the absence of an intervention, we would have expected an average response of 1.62K. The 95% interval of this counterfactual prediction is [1.57K, 1.66K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.13K with a 95% interval of [0.09K, 0.18K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 17.46K. By contrast, had the intervention not taken place, we would have expected a sum of 16.15K. The 95% interval of this prediction is [15.71K, 16.59K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +8%. The 95% interval of this percentage is [+5%, +11%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.13K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
New.York
During the post-intervention period, the response variable had an average value of approx. 17.06K. By contrast, in the absence of an intervention, we would have expected an average response of 12.84K. The 95% interval of this counterfactual prediction is [12.55K, 13.13K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 4.22K with a 95% interval of [3.93K, 4.51K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 170.65K. By contrast, had the intervention not taken place, we would have expected a sum of 128.40K. The 95% interval of this prediction is [125.54K, 131.35K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +33%. The 95% interval of this percentage is [+31%, +35%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (4.22K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
North.Carolina
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Missing data.
North.Dakota
During the post-intervention period, the response variable had an average value of approx. 672.00. By contrast, in the absence of an intervention, we would have expected an average response of 523.98. The 95% interval of this counterfactual prediction is [504.30, 543.39]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 148.02 with a 95% interval of [128.61, 167.70]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 6.72K. By contrast, had the intervention not taken place, we would have expected a sum of 5.24K. The 95% interval of this prediction is [5.04K, 5.43K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +28%. The 95% interval of this percentage is [+25%, +32%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (148.02) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Ohio
During the post-intervention period, the response variable had an average value of approx. 11.29K. By contrast, in the absence of an intervention, we would have expected an average response of 10.23K. The 95% interval of this counterfactual prediction is [9.96K, 10.48K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.06K with a 95% interval of [0.81K, 1.33K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 112.89K. By contrast, had the intervention not taken place, we would have expected a sum of 102.32K. The 95% interval of this prediction is [99.58K, 104.81K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +10%. The 95% interval of this percentage is [+8%, +13%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.06K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Oklahoma
During the post-intervention period, the response variable had an average value of approx. 3.61K. By contrast, in the absence of an intervention, we would have expected an average response of 3.36K. The 95% interval of this counterfactual prediction is [3.26K, 3.45K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.25K with a 95% interval of [0.16K, 0.35K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 36.10K. By contrast, had the intervention not taken place, we would have expected a sum of 33.60K. The 95% interval of this prediction is [32.62K, 34.47K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +7%. The 95% interval of this percentage is [+5%, +10%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.25K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Oregon
During the post-intervention period, the response variable had an average value of approx. 3.28K. By contrast, in the absence of an intervention, we would have expected an average response of 3.06K. The 95% interval of this counterfactual prediction is [2.98K, 3.15K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.21K with a 95% interval of [0.13K, 0.30K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 32.77K. By contrast, had the intervention not taken place, we would have expected a sum of 30.64K. The 95% interval of this prediction is [29.82K, 31.46K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +7%. The 95% interval of this percentage is [+4%, +10%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.21K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Pennsylvania
During the post-intervention period, the response variable had an average value of approx. 12.39K. By contrast, in the absence of an intervention, we would have expected an average response of 11.08K. The 95% interval of this counterfactual prediction is [10.82K, 11.33K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.31K with a 95% interval of [1.06K, 1.57K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 123.93K. By contrast, had the intervention not taken place, we would have expected a sum of 110.79K. The 95% interval of this prediction is [108.18K, 113.35K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +12%. The 95% interval of this percentage is [+10%, +14%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.31K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Rhode.Island
During the post-intervention period, the response variable had an average value of approx. 983.60. By contrast, in the absence of an intervention, we would have expected an average response of 826.74. The 95% interval of this counterfactual prediction is [799.49, 851.35]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 156.86 with a 95% interval of [132.25, 184.11]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 9.84K. By contrast, had the intervention not taken place, we would have expected a sum of 8.27K. The 95% interval of this prediction is [7.99K, 8.51K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+16%, +22%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (156.86) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
South.Carolina
During the post-intervention period, the response variable had an average value of approx. 4.95K. By contrast, in the absence of an intervention, we would have expected an average response of 4.22K. The 95% interval of this counterfactual prediction is [4.10K, 4.33K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.73K with a 95% interval of [0.62K, 0.86K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 49.55K. By contrast, had the intervention not taken place, we would have expected a sum of 42.23K. The 95% interval of this prediction is [40.99K, 43.34K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +17%. The 95% interval of this percentage is [+15%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.73K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
South.Dakota
During the post-intervention period, the response variable had an average value of approx. 762.40. By contrast, in the absence of an intervention, we would have expected an average response of 656.82. The 95% interval of this counterfactual prediction is [627.17, 684.48]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 105.58 with a 95% interval of [77.92, 135.23]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 7.62K. By contrast, had the intervention not taken place, we would have expected a sum of 6.57K. The 95% interval of this prediction is [6.27K, 6.84K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +16%. The 95% interval of this percentage is [+12%, +21%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (105.58) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Tennessee
During the post-intervention period, the response variable had an average value of approx. 7.07K. By contrast, in the absence of an intervention, we would have expected an average response of 5.96K. The 95% interval of this counterfactual prediction is [5.81K, 6.12K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.10K with a 95% interval of [0.95K, 1.26K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 70.66K. By contrast, had the intervention not taken place, we would have expected a sum of 59.62K. The 95% interval of this prediction is [58.09K, 61.19K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+16%, +21%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.10K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Texas
During the post-intervention period, the response variable had an average value of approx. 20.44K. By contrast, in the absence of an intervention, we would have expected an average response of 16.87K. The 95% interval of this counterfactual prediction is [16.38K, 17.35K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 3.57K with a 95% interval of [3.09K, 4.06K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 204.39K. By contrast, had the intervention not taken place, we would have expected a sum of 168.73K. The 95% interval of this prediction is [163.84K, 173.54K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +21%. The 95% interval of this percentage is [+18%, +24%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (3.57K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Utah
During the post-intervention period, the response variable had an average value of approx. 1.77K. By contrast, in the absence of an intervention, we would have expected an average response of 1.55K. The 95% interval of this counterfactual prediction is [1.50K, 1.59K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.22K with a 95% interval of [0.18K, 0.27K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 17.75K. By contrast, had the intervention not taken place, we would have expected a sum of 15.50K. The 95% interval of this prediction is [15.04K, 15.95K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+12%, +17%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.22K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Vermont
During the post-intervention period, the response variable had an average value of approx. 509.70. By contrast, in the absence of an intervention, we would have expected an average response of 486.08. The 95% interval of this counterfactual prediction is [466.38, 505.38]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 23.62 with a 95% interval of [4.32, 43.32]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 5.10K. By contrast, had the intervention not taken place, we would have expected a sum of 4.86K. The 95% interval of this prediction is [4.66K, 5.05K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +5%. The 95% interval of this percentage is [+1%, +9%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (23.62) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.012). This means the causal effect can be considered statistically significant.
Virginia
During the post-intervention period, the response variable had an average value of approx. 6.50K. By contrast, in the absence of an intervention, we would have expected an average response of 5.80K. The 95% interval of this counterfactual prediction is [5.65K, 5.95K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.70K with a 95% interval of [0.55K, 0.85K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 65.02K. By contrast, had the intervention not taken place, we would have expected a sum of 58.02K. The 95% interval of this prediction is [56.50K, 59.48K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +12%. The 95% interval of this percentage is [+10%, +15%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.70K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Washington
During the post-intervention period, the response variable had an average value of approx. 5.18K. By contrast, in the absence of an intervention, we would have expected an average response of 4.79K. The 95% interval of this counterfactual prediction is [4.66K, 4.93K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.38K with a 95% interval of [0.25K, 0.52K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 51.79K. By contrast, had the intervention not taken place, we would have expected a sum of 47.94K. The 95% interval of this prediction is [46.58K, 49.29K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +8%. The 95% interval of this percentage is [+5%, +11%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.38K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
West.Virginia
During the post-intervention period, the response variable had an average value of approx. 1.93K. In the absence of an intervention, we would have expected an average response of 1.91K. The 95% interval of this counterfactual prediction is [1.86K, 1.96K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.02K with a 95% interval of [-0.03K, 0.07K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 19.31K. Had the intervention not taken place, we would have expected a sum of 19.14K. The 95% interval of this prediction is [18.56K, 19.64K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +1%. The 95% interval of this percentage is [-2%, +4%].
This means that, although the intervention appears to have caused a positive effect, this effect is not statistically significant when considering the entire post-intervention period as a whole. Individual days or shorter stretches within the intervention period may of course still have had a significant effect, as indicated whenever the lower limit of the impact time series (lower plot) was above zero. The apparent effect could be the result of random fluctuations that are unrelated to the intervention. This is often the case when the intervention period is very long and includes much of the time when the effect has already worn off. It can also be the case when the intervention period is too short to distinguish the signal from the noise. Finally, failing to find a significant effect can happen when there are not enough control variables or when these variables do not correlate well with the response variable during the learning period.
The probability of obtaining this effect by chance is p = 0.289. This means the effect may be spurious and would generally not be considered statistically significant.
Wisconsin
During the post-intervention period, the response variable had an average value of approx. 4.92K. By contrast, in the absence of an intervention, we would have expected an average response of 4.45K. The 95% interval of this counterfactual prediction is [4.35K, 4.56K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.47K with a 95% interval of [0.35K, 0.57K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 49.17K. By contrast, had the intervention not taken place, we would have expected a sum of 44.51K. The 95% interval of this prediction is [43.46K, 45.63K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +10%. The 95% interval of this percentage is [+8%, +13%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.47K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Wyoming
During the post-intervention period, the response variable had an average value of approx. 439.50. By contrast, in the absence of an intervention, we would have expected an average response of 395.36. The 95% interval of this counterfactual prediction is [375.64, 415.38]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 44.14 with a 95% interval of [24.12, 63.86]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 4.39K. By contrast, had the intervention not taken place, we would have expected a sum of 3.95K. The 95% interval of this prediction is [3.76K, 4.15K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +11%. The 95% interval of this percentage is [+6%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (44.14) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
Puerto.Rico
## Warning: Removed 240 row(s) containing missing values (geom_path).
## Missing data.
UK
deaths <- read.csv.zoo("uk/uk-deaths.csv", FUN=as.yearmon)
deaths <- window(deaths, start=as.yearmon("Jan 2010"), end=as.yearmon("Oct 2020"))
go(deaths)
EAST
During the post-intervention period, the response variable had an average value of approx. 5.33K. By contrast, in the absence of an intervention, we would have expected an average response of 4.66K. The 95% interval of this counterfactual prediction is [4.39K, 4.90K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.68K with a 95% interval of [0.43K, 0.95K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 53.35K. By contrast, had the intervention not taken place, we would have expected a sum of 46.58K. The 95% interval of this prediction is [43.89K, 49.02K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+9%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.68K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
LONDON
During the post-intervention period, the response variable had an average value of approx. 5.01K. By contrast, in the absence of an intervention, we would have expected an average response of 4.06K. The 95% interval of this counterfactual prediction is [3.89K, 4.25K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.94K with a 95% interval of [0.75K, 1.12K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 50.05K. By contrast, had the intervention not taken place, we would have expected a sum of 40.61K. The 95% interval of this prediction is [38.88K, 42.53K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +23%. The 95% interval of this percentage is [+19%, +28%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.94K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
EAST.MIDLANDS
During the post-intervention period, the response variable had an average value of approx. 4.22K. By contrast, in the absence of an intervention, we would have expected an average response of 3.68K. The 95% interval of this counterfactual prediction is [3.49K, 3.87K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.55K with a 95% interval of [0.35K, 0.74K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 42.23K. By contrast, had the intervention not taken place, we would have expected a sum of 36.76K. The 95% interval of this prediction is [34.87K, 38.75K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+9%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.55K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
WEST.MIDLANDS
During the post-intervention period, the response variable had an average value of approx. 5.31K. By contrast, in the absence of an intervention, we would have expected an average response of 4.47K. The 95% interval of this counterfactual prediction is [4.25K, 4.69K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.84K with a 95% interval of [0.62K, 1.06K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 53.14K. By contrast, had the intervention not taken place, we would have expected a sum of 44.71K. The 95% interval of this prediction is [42.54K, 46.92K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +19%. The 95% interval of this percentage is [+14%, +24%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.84K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
NORTH.EAST
During the post-intervention period, the response variable had an average value of approx. 2.63K. By contrast, in the absence of an intervention, we would have expected an average response of 2.28K. The 95% interval of this counterfactual prediction is [2.17K, 2.39K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.34K with a 95% interval of [0.23K, 0.45K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 26.26K. By contrast, had the intervention not taken place, we would have expected a sum of 22.83K. The 95% interval of this prediction is [21.74K, 23.91K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+10%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.34K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
NORTH.WEST
During the post-intervention period, the response variable had an average value of approx. 6.86K. By contrast, in the absence of an intervention, we would have expected an average response of 5.85K. The 95% interval of this counterfactual prediction is [5.59K, 6.09K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 1.01K with a 95% interval of [0.76K, 1.27K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 68.57K. By contrast, had the intervention not taken place, we would have expected a sum of 58.48K. The 95% interval of this prediction is [55.88K, 60.93K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +17%. The 95% interval of this percentage is [+13%, +22%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (1.01K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
SOUTH.EAST
During the post-intervention period, the response variable had an average value of approx. 7.62K. By contrast, in the absence of an intervention, we would have expected an average response of 6.65K. The 95% interval of this counterfactual prediction is [6.32K, 6.96K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.97K with a 95% interval of [0.66K, 1.30K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 76.18K. By contrast, had the intervention not taken place, we would have expected a sum of 66.51K. The 95% interval of this prediction is [63.17K, 69.62K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +15%. The 95% interval of this percentage is [+10%, +20%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.97K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
SOUTH.WEST
During the post-intervention period, the response variable had an average value of approx. 5.06K. By contrast, in the absence of an intervention, we would have expected an average response of 4.64K. The 95% interval of this counterfactual prediction is [4.42K, 4.87K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.43K with a 95% interval of [0.19K, 0.64K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 50.62K. By contrast, had the intervention not taken place, we would have expected a sum of 46.35K. The 95% interval of this prediction is [44.24K, 48.71K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +9%. The 95% interval of this percentage is [+4%, +14%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.43K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.003). This means the causal effect can be considered statistically significant.
WALES
During the post-intervention period, the response variable had an average value of approx. 3.01K. By contrast, in the absence of an intervention, we would have expected an average response of 2.72K. The 95% interval of this counterfactual prediction is [2.59K, 2.86K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.29K with a 95% interval of [0.15K, 0.42K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 30.10K. By contrast, had the intervention not taken place, we would have expected a sum of 27.20K. The 95% interval of this prediction is [25.87K, 28.56K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +11%. The 95% interval of this percentage is [+6%, +16%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.29K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
YORKSHIRE.AND.THE.HUMBER
During the post-intervention period, the response variable had an average value of approx. 4.88K. By contrast, in the absence of an intervention, we would have expected an average response of 4.27K. The 95% interval of this counterfactual prediction is [4.07K, 4.48K]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 0.61K with a 95% interval of [0.39K, 0.81K]. For a discussion of the significance of this effect, see below.
Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 48.77K. By contrast, had the intervention not taken place, we would have expected a sum of 42.71K. The 95% interval of this prediction is [40.66K, 44.83K].
The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +14%. The 95% interval of this percentage is [+9%, +19%].
This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (0.61K) to the original goal of the underlying intervention.
The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant.
